1. Field of the Invention
The invention relates generally to information storage, management and analytic tools and more particularly to a data system for qualifying and analyzing data for at least one business intelligence.
2. Description of Related Art
Currently, business leaders managing “build asset”/facilities portfolios often make mission-critical decisions using: 1) no data, 2) the wrong data, or 3) inaccurate data. Vendors nominally in this space include: business intelligence developers, consultants, integrated workplace management system vendors, computer-aided facilities management systems providers and others. Clients can use technology and services to optimize efficiency around a wide variety of facilities related business problems, from project management to lease administration to space utilization and occupancy. These offerings, however, are standardized and afford the client only limited ability to customize them. Moreover, they are designed for and constrained by the organizational “silo” in which they reside.
Existing systems specify the data requirements based on the data's expected relationship to a type of outcome or discrete task (i.e., energy efficiency, lease administration, etc.). Where the outcomes are multi-dimensional, the data points within those dimensions are ill defined. The client “value” set is often predefined and solving for non-standard or multi-dimensional definitions of value is not supported. For example, available tools fail to provide built asset portfolio planning tools that allow an education client to solve for their own definition of value, i.e., maximum teacher retention against declining CapEx and contracting building inventory. In short, no existing analytics engine correlates asset-related (A), resources/environment-related (E) and culture-related (C) data over time (T) to illustrate current performance, optimum performance and/or benchmark performance.
Accordingly, there exists a need for modern, on-demand technology to extract, classify, validate, qualify, analyze, store, enhance and display data related to multi-dimensional enterprise decision making with adjustable value definitions. There is a further need to provide systems and methods that take an actuarial approach to predictive modeling related to human performance, resource utilization/environmental factors and architectural data. Optimal performance is dependent on hundreds (if not thousands) of factors, many of which are E/C/A/T dependent. The complexity of these interactions and correlations calls for powerful methodologies and technology to provide insight and the basis for action.
Accordingly, there is a need for improved data systems, and their methods of use, for qualifying and analyzing data for at least one business intelligence. There is a further need for data systems, and their methods of use, for qualifying and analyzing data for at least one business intelligence that uses multi-dimensional analysis relative to a scale for at least one business intelligence.